'''simple docstring'''




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__UpperCAmelCase					=re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
__UpperCAmelCase					=1_0
__UpperCAmelCase					=2_5_6






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:





    if len(A_							) < MIN_NUM_TOKENS:
        return None
    __lowerCamelCase            =    MinHash(num_perm=A_							)
    for token in set(A_							):
        min_hash.update(token.encode()							)
    return min_hash






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    return {t for t in NON_ALPHA.split(A_							) if len(t.strip()							) > 0}




class        a__ :






     def __init__(					self						:						List[Any]    ,							*,
         a						:						float = 0.85    ,							):





         """simple docstring"""



         __lowerCamelCase            =    duplication_jaccard_threshold
         __lowerCamelCase            =    NUM_PERM
         __lowerCamelCase            =    MinHashLSH(threshold=self._duplication_jaccard_threshold    ,							num_perm=self._num_perm       )

         __lowerCamelCase            =    defaultdict(UpperCamelCase__       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						Tuple    ,							a						:						MinHash       ):





         """simple docstring"""



         __lowerCamelCase            =    self._index.query(UpperCamelCase__       )
         if code_key in self._index.keys:
             print(f"""Duplicate key {code_key}"""       )
             return

         self._index.insert(UpperCamelCase__    ,							UpperCamelCase__       )
         if len(UpperCamelCase__       ) > 0:
             for base_duplicate in close_duplicates:
                 if base_duplicate in self._duplicate_clusters:
                     self._duplicate_clusters[base_duplicate].add(UpperCamelCase__       )
                     break
             else:
                 self._duplicate_clusters[close_duplicates[0]].add(UpperCamelCase__       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         for base, duplicates in self._duplicate_clusters.items():
             __lowerCamelCase            =    [base] + list(UpperCamelCase__       )
             # reformat the cluster to be a list of dict
             __lowerCamelCase            =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
             duplicate_clusters.append(UpperCamelCase__       )
         return duplicate_clusters






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.get_duplicate_clusters()
         with open(UpperCamelCase__    ,							'''w'''       ) as f:
             json.dump(UpperCamelCase__    ,							UpperCamelCase__       )






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Tuple:
    __lowerCamelCase      ,    __lowerCamelCase            =    element
    __lowerCamelCase            =    get_min_hash([t for t in NON_ALPHA.split(data['''content''']							) if len(t.strip()							) > 0]							)
    if min_hash is not None:
        return (index, data["repo_name"], data["path"]), min_hash






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:





    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash							,    ThreadedIterator(A_							,    max_queue_size=1_00_00							)							,    chunksize=1_00							,    ):
            if data is not None:
                yield data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple:
    __lowerCamelCase            =    DuplicationIndex(duplication_jaccard_threshold=A_							)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A_							)							)							,    max_queue_size=1_00							)							):
        di.add(A_							,    A_							)

    # Returns a List[Cluster] where Cluster is List[str] with the filenames.
    return di.get_duplicate_clusters()






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[int]:
    __lowerCamelCase            =    get_tokens(A_							)
    __lowerCamelCase            =    get_tokens(A_							)
    return len(tokensa & tokensa							) / len(tokensa | tokensa							)


__UpperCAmelCase					=None






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Any:
    __lowerCamelCase            =    []
    for elementa in cluster:
        __lowerCamelCase            =    _shared_dataset[elementa['''base_index''']]['''content''']
        for elementa in extremes:
            __lowerCamelCase            =    _shared_dataset[elementa['''base_index''']]['''content''']
            if jaccard_similarity(A_							,    A_							) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            __lowerCamelCase            =    1
            extremes.append(A_							)
    return extremes






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Dict:
    global _shared_dataset
    __lowerCamelCase            =    dataset
    __lowerCamelCase            =    []
    __lowerCamelCase            =    partial(_find_cluster_extremes_shared							,    jaccard_threshold=A_							)
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                A_							,    A_							,    )							,    total=len(A_							)							,    ):
            extremes_list.append(A_							)
    return extremes_list






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__ = 0.8_5							)      ->       Optional[Any]:
    __lowerCamelCase            =    make_duplicate_clusters(A_							,    A_							)
    __lowerCamelCase            =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    __lowerCamelCase            =    {}
    __lowerCamelCase            =    find_extremes(A_							,    A_							,    A_							)
    for extremes in extremes_clusters:
        for element in extremes:
            __lowerCamelCase            =    element
    __lowerCamelCase            =    duplicate_indices - set(extreme_dict.keys()							)
    __lowerCamelCase            =    dataset.filter(lambda UpperCamelCase__							,    UpperCamelCase__							: idx not in remove_indices							,    with_indices=A_							)

    # update duplicate_clusters
    for cluster in duplicate_clusters:
        for element in cluster:
            __lowerCamelCase            =    element['''base_index'''] in extreme_dict
            if element["is_extreme"]:
                __lowerCamelCase            =    extreme_dict[element['''base_index''']]['''copies''']

    print(f"""Original dataset size: {len(A_							)}"""							)
    print(f"""Number of duplicate clusters: {len(A_							)}"""							)
    print(f"""Files in duplicate cluster: {len(A_							)}"""							)
    print(f"""Unique files in duplicate cluster: {len(A_							)}"""							)
    print(f"""Filtered dataset size: {len(A_							)}"""							)

    return ds_filter, duplicate_clusters


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase     :	List[str]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[Any]       			=  OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__lowerCAmelCase     :	Optional[Any]       			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def a__       (			A_   ):






  '''simple docstring'''





  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      __magic_name__								=			model_type_to_module_name(A_   )

      __magic_name__								=			importlib.import_module(f'''.{module_name}''',	"""transformers.models"""   )
      try:
        return getattr(A_,	A_   )
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(A_,	"""__name__""",	A_   ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  __magic_name__								=			importlib.import_module("""transformers"""   )
  if hasattr(A_,	A_   ):
    return getattr(A_,	A_   )

  return None

def a__       (			A_,	A_ = None,	A_ = False,	A_ = False,	A_ = None,	A_ = None,	A_ = None,	A_ = False,	**A_,	):






  '''simple docstring'''





  __magic_name__								=			get_file_from_repo(
      A_,	A_,	cache_dir=A_,	force_download=A_,	resume_download=A_,	proxies=A_,	use_auth_token=A_,	revision=A_,	local_files_only=A_,	)
  if resolved_config_file is None:
    logger.info(
        """Could not locate the feature extractor configuration file, will try to use the model config instead."""   )
    return {}

  with open(A_,	encoding="""utf-8"""   ) as reader:
    return json.load(A_   )





class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    raise EnvironmentError(
        """AutoFeatureExtractor is designed to be instantiated """
        """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""       )
  @classmethod
  @replace_list_option_in_docstrings(UpperCamelCase__       )
  def 				_lowercase				(					cls     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Optional[int]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""config"""     ,				UpperCamelCase__       )
    __magic_name__								=			kwargs.pop("""trust_remote_code"""     ,				UpperCamelCase__       )
    __magic_name__								=			True

    __magic_name__	,       __magic_name__								=			FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    __magic_name__								=			config_dict.get("""feature_extractor_type"""     ,				UpperCamelCase__       )
    __magic_name__								=			None
    if "AutoFeatureExtractor" in config_dict.get("""auto_map"""     ,				{}       ):
      __magic_name__								=			config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

    # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
    if feature_extractor_class is None and feature_extractor_auto_map is None:
      if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			AutoConfig.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__       )
      # It could be in `config.feature_extractor_type``
      __magic_name__								=			getattr(UpperCamelCase__     ,				"""feature_extractor_type"""     ,				UpperCamelCase__       )
      if hasattr(UpperCamelCase__     ,				"""auto_map"""       ) and "AutoFeatureExtractor" in config.auto_map:
        __magic_name__								=			config.auto_map["""AutoFeatureExtractor"""]

    if feature_extractor_class is not None:
      __magic_name__								=			feature_extractor_class_from_name(UpperCamelCase__       )

    __magic_name__								=			feature_extractor_auto_map is not None
    __magic_name__								=			feature_extractor_class is not None or type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING
    __magic_name__								=			resolve_trust_remote_code(
        UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

    if has_remote_code and trust_remote_code:
      __magic_name__								=			get_class_from_dynamic_module(
          UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__       )
      __magic_name__								=			kwargs.pop("""code_revision"""     ,				UpperCamelCase__       )
      if os.path.isdir(UpperCamelCase__       ):
        feature_extractor_class.register_for_auto_class()
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    elif feature_extractor_class is not None:
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
    elif type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING:
      __magic_name__								=			FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase__       )]
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )

    raise ValueError(
        F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
        F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
        F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()       )}'''       )





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase__     ,				UpperCamelCase__       )

